def main():
args = parse_args()
assert args.out or args.show or args.json_out, \
('Please specify at least one operation (save or show the results) '
'with the argument "--out" or "--show" or "--json_out"')
if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
raise ValueError('The output file must be a pkl file.')
if args.json_out is not None and args.json_out.endswith('.json'):
args.json_out = args.json_out[:-5]
cfg = mmcv.Config.fromfile(args.config)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
cfg.model.pretrained = None
cfg.data.test.test_mode = True
# init distributed env first, since logger depends on the dist info.
if args.launcher == 'none':
distributed = False
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
# build the dataloader
# TODO: support multiple images per gpu (only minor changes are needed)
dataset = build_dataset(cfg.data.test)
data_loader = build_dataloader(dataset,
imgs_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=distributed,
shuffle=False)
# build the model and load checkpoint
model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
wrap_fp16_model(model)
while not osp.isfile(args.checkpoint):
print('Waiting for {} to exist...'.format(args.checkpoint))
time.sleep(60)
checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')
# old versions did not save class info in checkpoints, this walkaround is
# for backward compatibility
if 'CLASSES' in checkpoint['meta']:
model.CLASSES = checkpoint['meta']['CLASSES']
else:
model.CLASSES = dataset.CLASSES
if not distributed:
model = MMDataParallel(model, device_ids=[0])
outputs = single_gpu_test(model, data_loader)
else:
model = MMDistributedDataParallel(model.cuda())
outputs = multi_gpu_test(model, data_loader, args.tmpdir)
print(outputs)
rank, _ = get_dist_info()
if args.out and rank == 0:
print('\nwriting results to {}'.format(args.out))
mmcv.dump(outputs, args.out)
eval_types = args.eval
if eval_types:
print('Starting evaluate {}'.format(' and '.join(eval_types)))
if eval_types == ['proposal_fast']:
result_file = args.out
coco_eval(result_file,
eval_types,
dataset.coco,
classwise=args.classwise)
else:
if not isinstance(outputs[0], dict):
result_files = results2json_segm(dataset, outputs,
args.out)
coco_eval(result_files,
eval_types,
dataset.coco,
classwise=args.classwise)
else:
for name in outputs[0]:
print('\nEvaluating {}'.format(name))
outputs_ = [out[name] for out in outputs]
result_file = args.out + '.{}'.format(name)
result_files = results2json(dataset, outputs_,
result_file)
coco_eval(result_files,
eval_types,
dataset.coco,
classwise=args.classwise)
# Save predictions in the COCO json format
if args.json_out and rank == 0:
if not isinstance(outputs[0], dict):
results2json(dataset, outputs, args.json_out)
else:
for name in outputs[0]:
outputs_ = [out[name] for out in outputs]
result_file = args.json_out + '.{}'.format(name)
results2json(dataset, outputs_, result_file)
def _non_dist_train(model,
dataset,
cfg,
validate=False,
logger=None,
timestamp=None):
if validate:
raise NotImplementedError('Built-in validation is not implemented '
'yet in not-distributed training. Use '
'distributed training or test.py and '
'*eval.py scripts instead.')
# prepare data loaders
dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
data_loaders = [
build_dataloader(
ds,
cfg.data.imgs_per_gpu,
cfg.data.workers_per_gpu,
cfg.gpus,
dist=False) for ds in dataset
]
# put model on gpus
model = MMDataParallel(model, device_ids=range(cfg.gpus)).cuda()
# # if model.module.bbox_head.freeze_solov2_and_train_combonly:
# if model.module.bbox_head.optimize_list is not None:
# for (key, param) in model.named_parameters():
# # if 'kernel_convs_convcomb' not in key and 'context_fusion_convs' not in key and 'learned_weight' not in key:
# if not any(s in key for s in model.module.bbox_head.optimize_list):
# param.requires_grad=False
# else:
# # print('optimize {}'.format(key))
# logger.info('optimize {}'.format(key))
# build runner
optimizer = build_optimizer(model, cfg.optimizer)
runner = Runner(
model, batch_processor, optimizer, cfg.work_dir, logger=logger)
# an ugly walkaround to make the .log and .log.json filenames the same
runner.timestamp = timestamp
# fp16 setting
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
optimizer_config = Fp16OptimizerHook(
**cfg.optimizer_config, **fp16_cfg, distributed=False)
else:
optimizer_config = cfg.optimizer_config
runner.register_training_hooks(cfg.lr_config, optimizer_config,
cfg.checkpoint_config, cfg.log_config)
if cfg.resume_from:
runner.resume(cfg.resume_from)
elif cfg.load_from:
runner.load_checkpoint(cfg.load_from)
runner.run(data_loaders, cfg.workflow, cfg.total_epochs)
## add test after training
if cfg.data.test.ann_file != 'data/lvis/lvis_v0.5_val_lvis_freqset.json': # if val set is lvis freq, only eval on lvis-freq val set
cfg.data.test.test_mode = True
dataset = build_dataset(cfg.data.test)
data_loader = build_dataloader(
dataset,
imgs_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=False,
shuffle=False)
model_orig=model.module
model = MMDataParallel(model, device_ids=[0]).cuda()
data_loader.dataset.img_infos = data_loader.dataset.img_infos[:100]
outputs = single_gpu_test(model, data_loader)
print('\nwriting results to {}'.format('xxx'))
# mmcv.dump(outputs, 'xxx')
eval_types = ['segm']
if eval_types:
print('Starting evaluate {}'.format(' and '.join(eval_types)))
if eval_types == ['proposal_fast']:
result_file = 'xxx'
coco_eval(result_file, eval_types, dataset.coco)
else:
if not isinstance(outputs[0], dict):
result_files = results2json_segm(dataset, outputs, 'xxx', dump=False)
coco_eval(result_files, eval_types, dataset.coco)
else:
for name in outputs[0]:
print('\nEvaluating {}'.format(name))
outputs_ = [out[name] for out in outputs]
result_file = 'xxx' + '.{}'.format(name)
result_files = results2json(dataset, outputs_,
result_file, dump=False)
coco_eval(result_files, eval_types, dataset.coco)
##eval on lvis-77######
cfg.data.test.ann_file = 'data/lvis/lvis_v0.5_val_cocofied.json'
cfg.data.test.img_prefix = 'data/lvis/val2017/'
cfg.data.test.test_mode = True
dataset = build_dataset(cfg.data.test)
data_loader = build_dataloader(
dataset,
imgs_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=False,
shuffle=False)
# model_orig=model.module
# model = MMDataParallel(model, device_ids=[0]).cuda()
data_loader.dataset.img_infos = data_loader.dataset.img_infos[:100]
outputs = single_gpu_test(model, data_loader)
print('\nwriting results to {}'.format('xxx'))
# mmcv.dump(outputs, 'xxx')
eval_types = ['segm']
if eval_types:
print('Starting evaluate {}'.format(' and '.join(eval_types)))
if eval_types == ['proposal_fast']:
result_file = 'xxx'
coco_eval(result_file, eval_types, dataset.coco)
else:
if not isinstance(outputs[0], dict):
result_files = results2json_segm(dataset, outputs, 'xxx', dump=False)
from lvis import LVISEval
lvisEval = LVISEval('data/lvis/lvis_v0.5_val_cocofied.json', result_files, 'segm')
lvisEval.run()
lvisEval.print_results()
# fix lvis api eval iou_thr error, should be 0.9 but was 0.8999
lvisEval.params.iou_thrs[8] = 0.9
for iou in [0.5, 0.6, 0.7, 0.8, 0.9]:
print('AP at iou {}: {}'.format(iou, lvisEval._summarize('ap', iou_thr=iou)))
else:
for name in outputs[0]:
print('\nEvaluating {}'.format(name))
outputs_ = [out[name] for out in outputs]
result_file = 'xxx' + '.{}'.format(name)
result_files = results2json(dataset, outputs_,
result_file, dump=False)
coco_eval(result_files, eval_types, dataset.coco)
else:
##eval on lvis-freq######
cfg.data.test.test_mode = True
dataset = build_dataset(cfg.data.test)
data_loader = build_dataloader(
dataset,
imgs_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=False,
shuffle=False)
# model_orig=model.module
# model = MMDataParallel(model, device_ids=[0]).cuda()
data_loader.dataset.img_infos = data_loader.dataset.img_infos[:100]
outputs = single_gpu_test(model, data_loader)
print('\nwriting results to {}'.format('xxx'))
# mmcv.dump(outputs, 'xxx')
eval_types = ['segm']
if eval_types:
print('Starting evaluate {}'.format(' and '.join(eval_types)))
if eval_types == ['proposal_fast']:
result_file = 'xxx'
coco_eval(result_file, eval_types, dataset.coco)
else:
if not isinstance(outputs[0], dict):
result_files = results2json_segm(dataset, outputs, 'xxx', dump=False)
from lvis import LVISEval
lvisEval = LVISEval(cfg.data.test.ann_file, result_files, 'segm')
lvisEval.run()
lvisEval.print_results()
# fix lvis api eval iou_thr error, should be 0.9 but was 0.8999
lvisEval.params.iou_thrs[8] = 0.9
for iou in [0.5, 0.6, 0.7, 0.8, 0.9]:
print('AP at iou {}: {}'.format(iou, lvisEval._summarize('ap', iou_thr=iou)))
else:
for name in outputs[0]:
print('\nEvaluating {}'.format(name))
outputs_ = [out[name] for out in outputs]
result_file = 'xxx' + '.{}'.format(name)
result_files = results2json(dataset, outputs_,
result_file, dump=False)
coco_eval(result_files, eval_types, dataset.coco)
def main():
args = parse_args()
assert args.out or args.show or args.json_out, \
('Please specify at least one operation (save or show the results) '
'with the argument "--out" or "--show" or "--json_out"')
if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
raise ValueError('The output file must be a pkl file.')
if args.json_out is not None and args.json_out.endswith('.json'):
args.json_out = args.json_out[:-5]
cfg = mmcv.Config.fromfile(args.config)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
cfg.model.pretrained = None
cfg.data.test.test_mode = True
# init distributed env first, since logger depends on the dist info.
if args.launcher == 'none':
distributed = False
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
# build the dataloader
# TODO: support multiple images per gpu (only minor changes are needed)
dataset = build_dataset(cfg.data.test)
data_loader = build_dataloader(dataset,
imgs_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=distributed,
shuffle=False)
# build the model and load checkpoint
model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
wrap_fp16_model(model)
while not osp.isfile(args.checkpoint):
print('Waiting for {} to exist...'.format(args.checkpoint))
time.sleep(60)
checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')
# old versions did not save class info in checkpoints, this walkaround is
# for backward compatibility
if 'CLASSES' in checkpoint['meta']:
model.CLASSES = checkpoint['meta']['CLASSES']
else:
model.CLASSES = dataset.CLASSES
# assert not distributed
if not distributed:
model = MMDataParallel(model, device_ids=[0])
# data_loader.dataset.img_infos = data_loader.dataset.img_infos[:10]
outputs = single_gpu_test(model, data_loader)
else:
model = MMDistributedDataParallel(model.cuda())
outputs = multi_gpu_test(model, data_loader, args.tmpdir)
rank, _ = get_dist_info()
if args.out and rank == 0:
print('\nwriting results to {}'.format(args.out))
mmcv.dump(outputs, args.out)
eval_types = args.eval
if eval_types:
print('Starting evaluate {}'.format(' and '.join(eval_types)))
if eval_types == ['proposal_fast']:
result_file = args.out
coco_eval(result_file, eval_types, dataset.coco)
else:
if not isinstance(outputs[0], dict):
if dataset.ann_file == 'data/coco/annotations/image_info_test-dev2017.json':
result_files = results2json_segm(dataset,
outputs,
args.out,
dump=True)
else:
result_files = results2json_segm(dataset,
outputs,
args.out,
dump=False)
if 'lvis' in dataset.ann_file: ## an ugly fix to make it compatible with coco eval
from lvis import LVISEval
lvisEval = LVISEval(cfg.data.test.ann_file,
result_files, 'segm')
lvisEval.run()
lvisEval.print_results()
#fix lvis api eval iou_thr error, should be 0.9 but was 0.8999
lvisEval.params.iou_thrs[8] = 0.9
for iou in [0.5, 0.6, 0.7, 0.8, 0.9]:
print('AP at iou {}: {}'.format(
iou, lvisEval._summarize('ap', iou_thr=iou)))
else:
coco_eval(result_files, eval_types, dataset.coco)
else:
for name in outputs[0]:
print('\nEvaluating {}'.format(name))
outputs_ = [out[name] for out in outputs]
result_file = args.out + '.{}'.format(name)
result_files = results2json(dataset,
outputs_,
result_file,
dump=False)
coco_eval(result_files, eval_types, dataset.coco)
##eval on lvis-77######
cfg.data.test.ann_file = 'data/lvis/lvis_v0.5_val_cocofied.json'
cfg.data.test.img_prefix = 'data/lvis/val2017/'
cfg.data.test.test_mode = True
dataset = build_dataset(cfg.data.test)
data_loader = build_dataloader(
dataset,
imgs_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=False,
shuffle=False)
# model_orig=model.module
# model = MMDataParallel(model, device_ids=[0]).cuda()
# data_loader.dataset.img_infos = data_loader.dataset.img_infos[:10]
outputs = single_gpu_test(model, data_loader)
print('\nwriting results to {}'.format('xxx'))
# mmcv.dump(outputs, 'xxx')
eval_types = ['segm']
if eval_types:
print('Starting evaluate {}'.format(' and '.join(eval_types)))
if eval_types == ['proposal_fast']:
result_file = 'xxx'
coco_eval(result_file, eval_types, dataset.coco)
else:
if not isinstance(outputs[0], dict):
result_files = results2json_segm(dataset,
outputs,
'xxx',
dump=False)
from lvis import LVISEval
lvisEval = LVISEval(
'data/lvis/lvis_v0.5_val_cocofied.json', result_files,
'segm')
lvisEval.run()
lvisEval.print_results()
# fix lvis api eval iou_thr error, should be 0.9 but was 0.8999
lvisEval.params.iou_thrs[8] = 0.9
for iou in [0.5, 0.6, 0.7, 0.8, 0.9]:
print('AP at iou {}: {}'.format(
iou, lvisEval._summarize('ap', iou_thr=iou)))
else:
for name in outputs[0]:
print('\nEvaluating {}'.format(name))
outputs_ = [out[name] for out in outputs]
result_file = 'xxx' + '.{}'.format(name)
result_files = results2json(dataset,
outputs_,
result_file,
dump=False)
coco_eval(result_files, eval_types, dataset.coco)
# Save predictions in the COCO json format
if args.json_out and rank == 0:
if not isinstance(outputs[0], dict):
results2json(dataset, outputs, args.json_out)
else:
for name in outputs[0]:
outputs_ = [out[name] for out in outputs]
result_file = args.json_out + '.{}'.format(name)
results2json(dataset, outputs_, result_file)
def main():
args = parse_args()
assert args.out or args.show or args.json_out or args.vdo_out_folder, \
('Please specify at least one operation (save or show the results) '
'with the argument "--out" or "--show" or "--json_out"')
if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
raise ValueError('The output file must be a pkl file.')
if args.json_out is not None and args.json_out.endswith('.json'):
args.json_out = args.json_out[:-5]
cfg = mmcv.Config.fromfile(args.config)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
cfg.model.pretrained = None
cfg.data.test.test_mode = True
# init distributed env first, since logger depends on the dist info.
if args.launcher == 'none':
distributed = False
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
# build the dataloader
# TODO: support multiple images per gpu (only minor changes are needed)
dataset = build_dataset(cfg.data.test)
data_loader = build_dataloader(dataset,
imgs_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=distributed,
shuffle=False)
# build the model and load checkpoint
model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
wrap_fp16_model(model)
while not osp.isfile(args.checkpoint):
print('Waiting for {} to exist...'.format(args.checkpoint))
time.sleep(60)
checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')
# old versions did not save class info in checkpoints, this walkaround is
# for backward compatibility
if 'CLASSES' in checkpoint['meta']:
model.CLASSES = checkpoint['meta']['CLASSES']
else:
model.CLASSES = dataset.CLASSES
if not distributed:
model = MMDataParallel(model, device_ids=[0])
outputs = single_gpu_test(model, data_loader)
else:
model = MMDistributedDataParallel(model.cuda())
outputs = multi_gpu_test(model, data_loader, args.tmpdir)
rank, _ = get_dist_info()
if args.out and rank == 0:
print('\nwriting results to {}'.format(args.out))
mmcv.dump(outputs, args.out)
eval_types = args.eval
if eval_types:
print('Starting evaluate {}'.format(' and '.join(eval_types)))
if eval_types == ['proposal_fast']:
result_file = args.out
coco_eval(result_file, eval_types, dataset.coco)
else:
if not isinstance(outputs[0], dict):
result_files = results2json_segm(dataset, outputs,
args.out)
coco_eval(result_files, eval_types, dataset.coco)
else:
for name in outputs[0]:
print('\nEvaluating {}'.format(name))
outputs_ = [out[name] for out in outputs]
result_file = args.out + '.{}'.format(name)
result_files = results2json(dataset, outputs_,
result_file)
coco_eval(result_files, eval_types, dataset.coco)
# Save predictions in the COCO json format
if args.json_out and rank == 0:
if not isinstance(outputs[0], dict):
results2json(dataset, outputs, args.json_out)
else:
for name in outputs[0]:
outputs_ = [out[name] for out in outputs]
result_file = args.json_out + '.{}'.format(name)
results2json(dataset, outputs_, result_file)
# Save predictions in RLE format for VDO
'''
if args.vdo_out_folder and rank == 0:
if not osp.exists(args.vdo_out_folder):
os.mkdir(args.vdo_out_folder)
for i in range(len(dataset)):
img_id = dataset.img_infos[i]['id']
file_name = dataset.img_infos[i]['file_name']
width = dataset.img_infos[i]['width']
height = dataset.img_infos[i]['height']
results = outputs[i]
lines = ['{} {}\n'.format(width, height).encode()]
for class_id in range(len(results)):
for segm in results[class_id]:
lines.append('{} '.format(class_id).encode())
lines.append(segm[0]['counts'])
lines.append('\n'.encode())
out_file_name = '.'.join(file_name.split('.')[:-1] + ['txt'])
with open(osp.join(args.vdo_out_folder, out_file_name), 'wb') as f:
f.writelines(lines)
'''
# Save predictions in default format for VDO
if args.vdo_out_folder and rank == 0:
if not osp.exists(args.vdo_out_folder):
os.mkdir(args.vdo_out_folder)
for i in tqdm(range(len(dataset))):
file_name = dataset.img_infos[i]['file_name']
width = dataset.img_infos[i]['width']
height = dataset.img_infos[i]['height']
results = outputs[i]
mask = np.zeros((height, width), dtype=np.uint8)
obj_id = 1
for class_id in range(len(results)):
for segm in results[class_id]:
m = mask_util.decode(segm[0])
m = m * obj_id
mask[m > 0] = m[m > 0]
obj_id += 1
lines = list()
for y in range(mask.shape[0]):
line = str()
for x in range(mask.shape[1]):
line = line + str(mask[y][x]) + ' '
if y != mask.shape[0] - 1:
line = line + '\n'
lines.append(line)
out_file_name = '.'.join(file_name.split('.')[:-1] + ['txt'])
with open(osp.join(args.vdo_out_folder, out_file_name), 'w') as f:
f.writelines(lines)